For a lot of higher ed analysts, the scariest part of the job isn’t writing a PeopleSoft query—it’s what happens after the extract lands in Excel. Long columns to clean, messy values to fix, files to stack, and logic to repeat every census term or disbursement cycle. Many colleagues have lived mostly in PeopleSoft Query Manager and basic Excel, with little exposure to R, Python, or SAS, and suddenly find themselves asked to “prep the data” too. This post is for them.
Why Start with Power Query in Excel?
Power Query in Excel is a friendly, visual way to step into real data preparation without learning a programming language or buying a new BI tool. Power Query is built into modern versions of Excel under the Data tab, so you can start where you already live instead of adopting an entirely new platform. It lets you:
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Import from many sources (CSV exports from PeopleSoft, SQL Server reporting tables, Access databases, text files, web sources, etc.).
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Clean and reshape the data using a point‑and‑click interface instead of complex formulas or code.
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Automate repeatable prep so next term’s file is ready with a single refresh instead of another afternoon of copy‑paste.
For HEUG members who are being nudged toward more data wrangling, Power Query is often the easiest “first real data prep tool” because it feels like an extension of the Excel skills they already have.
How HEUG Technical & Reporting Communities Benefit
The HEUG Technical and Reporting communities focus on helping institutions get more value from PeopleSoft and related Oracle tools, often by improving how data flows into reports, dashboards, and integrations. Power Query fits right into that mission by giving both technical and reporting professionals a shared, accessible way to transform data without needing to build a full ETL process or deploy a new platform.
For technical members, Power Query can serve as a lightweight staging layer for proof‑of‑concepts, quick data investigations, or ad‑hoc integrations coming out of PeopleSoft, web services, or secondary databases, before anything is hardened into code. For reporting and analytics practitioners, it becomes the missing middle step between “I ran a PS Query” and “I have a reliable, analysis‑ready dataset,” reducing reliance on one-off Excel tricks and manual cleanup.
Because HEUG is a global community built on sharing practices and patterns, Power Query also provides a common language for exchanging concrete solutions—step lists, query definitions, and transformation logic that others can replicate in their own environments. Sessions and blogs that walk through a Power Query solution are easy for a wide range of members to adopt, since Excel is nearly universal across HEUG institutions. I am hoping this post encourages other folks who work with this tool to share their thoughts, recommendations, and solutions!
How Power Query Helps with Higher Ed Work
Here are concrete ways it can help with the kinds of tasks HEUG members see every day:
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Automate recurring term jobs
Build a query once to filter to active students, trim stray spaces, standardize career/program codes, and merge in a census snapshot. Next term, point to the new extract and hit refresh instead of redoing all the steps manually.
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Combine files from multiple sources
Stack multiple CSVs (e.g., one per college, campus, or term) into a single table in seconds, rather than copying and pasting tabs together.
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Fix common data quality issues
Remove duplicates, split concatenated fields, standardize values (such as “FT”, “Full Time”, “Full‑Time” to a single value), and safely change data types for dates and numbers.
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Reduce Excel formula sprawl
Logic that might have lived in layers of VLOOKUPs and nested IF statements can be captured as clear, step‑by‑step transformations that are easier to review and reproduce.
The big win is that Power Query records your steps. Each filter, split, merge, or transformation becomes a documented step that can be replayed and modified later, which is a huge help for auditability and collaboration with other analysts.
You Don’t Need to Know R, Python, or SAS
Many higher ed colleagues feel pressure to “catch up” with programming tools, but don’t have the time, support, or comfort level to dive into them right away. Power Query offers:
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A no‑code or low‑code environment where most work is done via menus and right‑clicks.
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The ability to grow into the underlying M language later, but only if and when you’re ready.
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A way to build good data prep habits—documented, repeatable, query‑driven workflows—that will transfer nicely if you eventually move into R, Python, or BI tools.
Think of Power Query as a bridge between “I clean everything manually in Excel” and “I script my entire pipeline.”
From a personal standpoint, learning Power Query turned many “ugh, not this again” tasks into structured, repeatable flows. Instead of spending hours re‑cleaning the same files each term, it became possible to invest that time once in a Power Query, then reuse and tweak it, which made space for deeper analysis and better conversations with functional partners.
Where to Start: A Simple First Project
A great first Power Query project is one that feels messy today but repeats every term. For many HEUG members, that looks like trying to answer a question such as, “Which students are in which programs, who are their current advisors, and what are those advisors’ faculty ranks?”
You might have:
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An enrollment or student program file (careers, programs, plans, majors, terms)
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An advisor–advisee assignment file
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A faculty or HR file with faculty ranks and maybe departments
In Power Query, your starter workflow could be:
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Load each source file
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Import your enrollment extract, advisor/advisee list, and faculty rankings into Power Query as separate queries.
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Keep just the columns you actually need (IDs, term, program/plan, advisor ID, rank, etc.).
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Clean each dataset
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Standardize ID formats, trim spaces, fix text casing, and set correct data types (especially IDs and dates).
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Rename columns so joins are obvious (for example, EMPLID for students, ADVISOR_ID for advisors, FACULTY_ID for faculty).
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Merge into a single model
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Merge the student program data with the advisor/advisee file on student ID and term (or current flag).
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Merge in the faculty rankings using the advisor’s ID to bring in rank and related attributes.
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Remove duplicates and filter to the “current” or relevant term/program records.
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Load a clean, analyzable table back to Excel
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Close & Load the final merged query into a worksheet as your “master” table for reporting, pivot tables, or downstream visualizations.
The next time you receive updated enrollment, advisor, and faculty files, you just point Power Query to the new files and click Refresh. Instead of redoing dozens of manual steps, you spend your time analyzing which programs lack advising coverage, how advisor ranks align with student outcomes, or which populations need more support.
In day‑to‑day work, this pattern shows up constantly—student success analyses, advising caseload reviews, faculty workload or ranking studies—and having it encapsulated in a reusable Power Query has made those projects faster to update and easier to explain to stakeholders.
Helpful Resources to Get You Started
Closing Thoughts and Call to Action
Power Query in Excel gives higher ed analysts a way to move from “I just pull the data” to “I own the whole pipeline” without jumping straight into a programming language. It lives in a tool you already use, lets you document each step of your cleanup, and turns painful, repetitive integration work—like pulling data from multiple systems—into a refreshable workflow instead of a weekend project.
If you’ve been feeling intimidated by data prep, start small: take your enrollment extract, your advisor/advisee file, and a faculty rankings file, and use Power Query to clean each one and merge them into a single, student‑ or course‑level view you can refresh each term. Then share what you learn—post a quick example in a HEUG Technical or Reporting community discussion, propose an Alliance session, or host a short brown‑bag where you rebuild this multi‑file process together. The sooner you take that first, low‑risk step, the sooner data wrangling stops being something to fear and starts being a core part of your analytical superpower.
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